Geologic models, such as petroleum reservoir geologic models, are often utilized by computer systems for simulations. For example, computer systems may utilize petroleum reservoir geologic models to simulate the flow and location of hydrocarbons within a reservoir. Geologic models are typically formed utilizing millions of three-dimensional elements or “cells,” with each cell corresponding to a location and a physical geologic feature. As there is a general correlation between the number of cells a model contains and the simulation accuracy provided by the model, it is generally desirable to form geologic models utilizing as many cells as possible.
Accurate reservoir performance forecasting requires three-dimensional representation of the geologic model. The geologic model is commonly built with the use of well data and stochastic simulation techniques. Simulated rock property values are filled in the three-dimensional cells constructed at a given scale. Cell dimensions are changed according to the needs of flow simulation. The cells can be “upscaled” into larger (“coarser”) cells, “downscaled” into smaller (“finer”) cells or a combination thereof.
Upscaling is used in reservoir modeling to speed up fluid-flow simulation by reducing the number of simulation cells of high resolution (or fine-scale) reservoir model. A good upscaling method not only preserves reservoir heterogeneity of the fine-scale model, but also maintains the accuracy of flow simulation. Conventional upscaling methods work well when the reservoirs are, or close to, homogeneous. When the reservoirs are highly heterogeneous and/or with complex cell connections, these methods fail to produce acceptable results.
For example, the static-based approach groups the fine cells by minimizing the statistical difference between the fine and coarse models, and then calculates the upscaled properties using analytical averaging methods. The static-based methods are CPU efficient, but may not be able to capture the flow behavior of the fine model. The flow based approach uses a pre-defined coarse framework to calculate the upscaled properties using either single or two phase flow simulations to mimic the flow behavior in the fine model. The flow-based approaches could better capture the dynamic flow behavior, but are CPU inefficient and are strongly dependent on many other dynamic parameter settings, such as well placement and production scenario.
Therefore, a need exists to reduce the simulation errors between the fine and coarse models and to preserve the flow barriers and the detoured fluid-flow paths.